path = "/gpfs/hpchome/a72094/rocket/projects/chromatin_to_splicing/"
Warning message:
no function found corresponding to methods exports from ‘XVector’ for: ‘concatenateObjects’ 
library("dplyr")

Attaching package: ‘dplyr’

The following objects are masked from ‘package:GenomicRanges’:

    intersect, setdiff, union

The following object is masked from ‘package:GenomeInfoDb’:

    intersect

The following objects are masked from ‘package:IRanges’:

    collapse, desc, intersect, setdiff, slice, union

The following objects are masked from ‘package:S4Vectors’:

    first, intersect, rename, setdiff, setequal, union

The following objects are masked from ‘package:BiocGenerics’:

    combine, intersect, setdiff, union

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library("readr")
library("devtools")
library("rtracklayer")
library("wiggleplotr")
library("GenomicRanges")
library('Rsamtools')
Loading required package: Biostrings
Loading required package: XVector

Attaching package: ‘Biostrings’

The following object is masked from ‘package:base’:

    strsplit
library('tidyr')

Attaching package: ‘tidyr’

The following object is masked from ‘package:S4Vectors’:

    expand
load_all("/gpfs/rocket/home/a72094/projects/chromatin_to_splicing/seqUtils/")
Loading seqUtils

Splaissimise andmed

#geenid, millel leidub splaiss QTL
splicing = read.table(paste0(path, "tabix/txrevise.significant.contained.sorted.txt.gz"))
#r2 seotud splass ja ca lookuste paarid
ca_splicing = read.table(paste0(path, "results/rsquared08/cqn_contained_rsq.txt"))

Kromatiini avatuse andmed

Aluseks peavad olema kromatiini avatuse kühmud, millel leidub: 1. statistiliselt oluline caQTL 25872 2. caQTL on seotud splaiss QTL’ga

Alustuseks leian kõik kühmud, mis vastavad kriteeriumitele

ca_interesting_peaks = ca[which(ca$V8 %in% ca_splicing$V1),]
Error: object 'ca' not found

Geenid, mille splaissimine on R2 seotud kromatiini avatusega

interesting_genes = splicing[which(splicing$V10 %in% ca_splicing$V4),c("V1", "V2", "V3","V4","V10","V12","V19","V21")]
colnames(interesting_genes) = c("gene_id", "gene_chr","gene_start","gene_end","gene_snpid", "gene_snp_pos","gene_slope","gene_pvalue")
interesting_genes

Seon kromatiini kühmud ja geenid

interesting = dplyr::left_join(interesting_genes, ca_splicing, by = c("gene_snpid"="V4"))[1:9] %>% left_join(interesting_peaks, ., by=c('peak_snpid'='V1'))
Column `gene_snpid`/`V4` joining factors with different levels, coercing to character vectorColumn `peak_snpid`/`V1` joining factors with different levels, coercing to character vector
interesting = dplyr::select(interesting, -one_of('ctcf_chr','gene_chr', 'peak_snp_chr'))
Unknown columns: `ctcf_chr`
interesting$gene_id = substr(interesting$gene_id, 1, nchar("ENSG00000171735.contained")-10)
interesting
write.table(interesting, file = paste0(path, 'interesting.txt'), col.names = T, quote = F, row.names = F, append = F)
interesting = read.table(paste0(path, 'interesting.txt'), header = TRUE)
filtered = interesting[which(abs(interesting$peak_slope)>0.4 & abs(interesting$gene_slope)>0.4 & interesting$ca_pvalue<1e-4 & interesting$gene_pvalue<1e-4),]
filtered

ATAC

#proovid
ATAC_sample_metadata = read.table(paste0(path,'QC_measures/run_sample_accession_PhaseIII.txt'))
colnames(ATAC_sample_metadata) = c("sample_id", "genotype_id")
ATAC_sample_metadata["condition_name"] = rep("naive", dim(ATAC_sample_metadata)[1])
#genotüübid
vcf_file = readRDS(paste0(path, 'genotypes/Kumasaka_100_samples.merged.rds'))
ATAC_meta_df = read.table(paste0(path, 'ATAC_meta_df'), header = TRUE)
ATAC_peak_metadata = read.table(paste0(path,'ATAC_peak_metadata'), header = TRUE)
regions_df = unique(filtered[,c('peak_id', 'peak_chr','peak_start', 'peak_end','peak_snpid','gene_id', 'peak_chr','gene_start', 'gene_end','gene_snpid','gene_snp_pos')])
colnames(regions_df) = c('peak_id', 'peak_chr', 'peak_start', 'peak_end', 'peak_rs_id', 'gene_id', 'gene_chr', 'gene_start', 'gene_end', 'gene_rs_id', 'gene_pos')
regions_df
#rm(vcf_file,ATAC_counts)

RNA

RNA_sample_metadata = read.table("/gpfs/hpchome/a72094/hpc/datasets/controlled_access/SampleArcheology/studies/cleaned/GEUVADIS.tsv", header = TRUE)[,c("sample_id","genotype_id","condition")]
colnames(RNA_sample_metadata)[3] = "condition_name"
if (FALSE){
  # RNA seq lugemite arv used for calculating library size
  RNA_counts = read.table("/gpfs/hpchome/a72094/hpc/projects/RNAseq_pipeline/results/expression_matrices/featureCounts/GEUVADIS.tsv.gz", header = TRUE)
  RNA_meta_df = wiggleplotrConstructMetadata(RNA_counts, RNA_sample_metadata, "/gpfs/hpc/home/a72094/projects/RNAseq_pipeline/processed/GEUVADIS/bigwig", bigWig_suffix = ".str1.bw", condition_name_levels = c("naive"))
  rm(RNA_counts)
  saveRDS(RNA_meta_df, paste0(path, 'RNA_meta_df'))
}
RNA_meta_df = readRDS(paste0(path, 'RNA_meta_df'))
RNA_meta_df
regions_df = unique(filtered[,c('gene_id', 'peak_chr','gene_start', 'gene_end','gene_snpid','gene_snp_pos')])
colnames(regions_df) = c('gene_id', 'chr', 'start', 'end', 'rs_id', 'pos')
regions_df

geenide annotatsioonid ja transkriptide annotatsioonid

gtf_df <- as.data.frame(rtracklayer::import(paste0(path, 'genotypes/Homo_sapiens.GRCh38.96.chr.gtf.gz')))
  filtered_annotations = gtf_df[gtf_df$gene_id %in% regions_df$gene_id,]
  filtered_annotations

  rm(gtf_df)
  saveRDS(filtered_annotations, paste0(path, 'filtered_annotations'))
filtered_annotations = readRDS(paste0(path, 'filtered_annotations'))
filtered_annotations

Koik geeni eksonid GRanges objektidena

find_peak_annotations = function(region_coords, chr, RNA_peak_metadata){
  RNA_peak_annot = wiggleplotrExtractPeaks(region_coords, chrom = chr, RNA_peak_metadata)
  RNA_peak_annot$peak_annot$transcript_id='RNA'
  RNA_peak_annot$peak_annot$gene_id = 'RNA'
  RNA_peak_annot$peak_annot$gene_name = 'RNA-seq'
  names(RNA_peak_annot$peak_list) = 'RNA'
  return(RNA_peak_annot)
}

Leian vcf failist loigu, mis vastab uuritavale geenile

find_genotype = function(gene_id, region_coords){
  library(GenomicRanges)
  gene_range = GRanges(seqnames =filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'seqnames'], strand = c("*"), ranges = IRanges(start = region_coords[1], end = region_coords[2], names = gene_id))
  vcf_file = paste0(path, 'genotypes/GEUVADIS_GRCh38_filtered.vcf.gz')
  genotype = scanTabixDataFrame(vcf_file, gene_range, col_names = FALSE)[[1]]
  #saveRDS(genotype, paste0(path, 'genotype'))
  
  #zgrep -m 1 "#CHROM" GEUVADIS_GRCh38_filtered.vcf.gz > vcf_genotype_id
  #vim vcf_genotype_id delete #
  
  v = scan(paste0(path, '/genotypes/vcf_genotype_id'), character(), quote = "")
  #unlink(paste0(path, '/genotypes/vcf_genotype_id'))
  colnames(genotype) = v
  
  genotype[genotype=="0|0"]<-0
  genotype[genotype=="1|0"]<-1
  genotype[genotype=="0|1"]<-1
  genotype[genotype=="1|1"]<-2
  
  return(genotype)
}
find_track_data = function(genotype, chr, RNA_meta_df){
  # add colour group
  colour_group = genotype[which(genotype$CHROM==chr & genotype$POS==pos), ] %>% select(10:454) %>% gather(., key= colnames(.), value=.[1,])
  colnames(colour_group) = c('genotype_id', 'colour_group')
  
  RNA_track_data = dplyr::left_join(RNA_meta_df, colour_group, by='genotype_id') %>% dplyr::mutate(track_id = "RNA-seq")
  RNA_track_data[, 'colour_group'] = as.factor(RNA_track_data[, 'colour_group'])
  return(RNA_track_data)
}

ATAC joonised

joint_plotlist = list()

for (i in 1:dim(regions_df)[1]){
  ATAC_rs_id = regions_df[i, 'peak_rs_id']
  ATAC_region_coords=c(regions_df[i,'peak_start']-1000, regions_df[i,'peak_end']+1000)
  chr = regions_df[i,'peak_chr']
  ATAC_peak_annot = wiggleplotrExtractPeaks(region_coords, chrom = chr, ATAC_peak_metadata)
  
  # add colour group
  ATAC_colour_group = data.frame(genotype_id = names(vcf_file$genotypes[ATAC_rs_id,]), colour_group=vcf_file$genotypes[rs_id,])
  ATAC_track_data = dplyr::left_join(ATAC_meta_df, ATAC_colour_group, by='genotype_id') %>% dplyr::mutate(track_id = "ATAC-seq")
  ATAC_track_data[, 'colour_group'] = as.factor(ATAC_track_data[, 'colour_group'])
  
  ATAC_coverage = plotCoverage(exons = ATAC_peak_annot$peak_list, cdss = ATAC_peak_annot$peak_list, track_data = ATAC_track_data, rescale_introns = FALSE, 
                             transcript_annotations = ATAC_peak_annot$peak_annot, fill_palette = getGenotypePalette(),
                             connect_exons = FALSE, transcript_label = FALSE, plot_fraction = 0.1, heights = c(0.7,0.3), 
                             region_coords = ATAC_region_coords, return_subplots_list = TRUE, coverage_type = "line")
  
  
  joint_plot = cowplot::plot_grid(ATAC_coverage$coverage_plot, 
                                ATAC_coverage$tx_structure,
                                align = "v", ncol = 1, rel_heights = c(3,2))
  
  joint_plotlist[[i]] = joint_plot
  print(joint_plot)
  ggsave(paste0('/gpfs/hpchome/evelin95/plots/ca_splicing/',regions_df[i,'peak_id'],'.pdf'))
  
}

RNA joonised

library(ggplot2)
RNA_coverage_plotlist = list()
for (i in 1:dim(regions_df)[1]){
  rs_id = regions_df[i, 'rs_id']
  pos = regions_df[i, 'pos']
  gene_id = regions_df[i, 'gene_id']
  region_coords = c(filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'start'], filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'end'])
  chr = regions_df[i,'chr']
  
  RNA_peak_metadata = filtered_annotations[which(filtered_annotations$type=='exon' & filtered_annotations$gene_id==gene_id),c("seqnames", "start", "end", "strand")]
  colnames(RNA_peak_metadata)[1]='chr'
  
  genotype=find_genotype(gene_id, region_coords)
  
  RNA_peak_annot = find_peak_annotations(region_coords, chr, RNA_peak_metadata)
  RNA_track_data = find_track_data(genotype, chr, RNA_meta_df)
  
  RNA_coverage = plotCoverage(exons = RNA_peak_annot$peak_list, cdss = RNA_peak_annot$peak_list, track_data = RNA_track_data, rescale_introns = TRUE, 
                             transcript_annotations = RNA_peak_annot$peak_annot, fill_palette = getGenotypePalette(),
                             connect_exons = FALSE, transcript_label = FALSE, plot_fraction = 0.1, heights = c(0.7,0.3), 
                             region_coords = region_coords, return_subplots_list = TRUE, coverage_type = "line")
  
  
  RNA_coverage_plotlist[[i]] = RNA_coverage$coverage_plot
  print(RNA_coverage$coverage_plot)
  ggsave(paste0('/gpfs/hpchome/evelin95/plots/ca_splicing/',gene_id,'.pdf'))
}
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---
title: "Kromatiini avatuse ja splaissimise seoste visualiseerimine"
output: html_notebook
---

```{r}
path = "/gpfs/hpchome/a72094/rocket/projects/chromatin_to_splicing/"
library("dplyr")
library("readr")
library("devtools")
library("rtracklayer")
library("wiggleplotr")
library("GenomicRanges")
library('Rsamtools')
library('tidyr')
load_all("/gpfs/rocket/home/a72094/projects/chromatin_to_splicing/seqUtils/")
```


Splaissimise andmed
```{r}
#geenid, millel leidub splaiss QTL
splicing = read.table(paste0(path, "tabix/txrevise.significant.contained.sorted.txt.gz"))

#r2 seotud splass ja ca lookuste paarid
ca_splicing = read.table(paste0(path, "results/rsquared08/cqn_contained_rsq.txt"))
```

Kromatiini avatuse andmed

Aluseks peavad olema kromatiini avatuse kühmud, millel leidub:
1. statistiliselt oluline caQTL 25872
2. caQTL on seotud splaiss QTL'ga 


Alustuseks leian kõik kühmud, mis vastavad kriteeriumitele
```{r}
ca_interesting_peaks = ca[which(ca$V8 %in% ca_splicing$V1),]
interesting_peaks = dplyr::select(ca_interesting_peaks, c("V1", "V2", "V3", "V4", "V8", "V9", "V10", "V17","V19"))
colnames(interesting_peaks) = c("peak_id","peak_chr", "peak_start", "peak_end", "peak_snpid", "peak_snp_chr", "peak_snp_pos", "peak_slope", "ca_pvalue")
interesting_peaks
```


Geenid, mille splaissimine on R2 seotud kromatiini avatusega
```{r}
interesting_genes = splicing[which(splicing$V10 %in% ca_splicing$V4),c("V1", "V2", "V3","V4","V10","V12","V19","V21")]
colnames(interesting_genes) = c("gene_id", "gene_chr","gene_start","gene_end","gene_snpid", "gene_snp_pos","gene_slope","gene_pvalue")
interesting_genes
```

```{r}
head(ca_splicing)
```



Seon kromatiini kühmud ja geenid
```{r}
interesting = dplyr::left_join(interesting_genes, ca_splicing, by = c("gene_snpid"="V4"))[1:9] %>% left_join(interesting_peaks, ., by=c('peak_snpid'='V1'))
interesting = dplyr::select(interesting, -one_of('ctcf_chr','gene_chr', 'peak_snp_chr'))
interesting$gene_id = substr(interesting$gene_id, 1, nchar("ENSG00000171735.contained")-10)
interesting
```

```{r}
write.table(interesting, file = paste0(path, 'interesting.txt'), col.names = T, quote = F, row.names = F, append = F)
```

```{r}
interesting = read.table(paste0(path, 'interesting.txt'), header = TRUE)
```

```{r}
filtered = interesting[which(abs(interesting$peak_slope)>0.4 & abs(interesting$gene_slope)>0.4 & interesting$ca_pvalue<1e-4 & interesting$gene_pvalue<1e-4),]
filtered
```


# ATAC
```{r}
#proovid
ATAC_sample_metadata = read.table(paste0(path,'QC_measures/run_sample_accession_PhaseIII.txt'))
colnames(ATAC_sample_metadata) = c("sample_id", "genotype_id")
ATAC_sample_metadata["condition_name"] = rep("naive", dim(ATAC_sample_metadata)[1])
```

```{r}
#genotüübid
vcf_file = readRDS(paste0(path, 'genotypes/Kumasaka_100_samples.merged.rds'))
```

```{r}
ATAC_meta_df = read.table(paste0(path, 'ATAC_meta_df'), header = TRUE)
ATAC_peak_metadata = read.table(paste0(path,'ATAC_peak_metadata'), header = TRUE)
```


```{r}
regions_df = unique(filtered[,c('peak_id', 'peak_chr','peak_start', 'peak_end','peak_snpid','gene_id', 'peak_chr','gene_start', 'gene_end','gene_snpid','gene_snp_pos')])
colnames(regions_df) = c('peak_id', 'peak_chr', 'peak_start', 'peak_end', 'peak_rs_id', 'gene_id', 'gene_chr', 'gene_start', 'gene_end', 'gene_rs_id', 'gene_pos')
regions_df
```


```{r}
#rm(vcf_file,ATAC_counts)
```

# RNA
```{r}
RNA_sample_metadata = read.table("/gpfs/hpchome/a72094/hpc/datasets/controlled_access/SampleArcheology/studies/cleaned/GEUVADIS.tsv", header = TRUE)[,c("sample_id","genotype_id","condition")]
colnames(RNA_sample_metadata)[3] = "condition_name"
```

```{r}
if (FALSE){
  # RNA seq lugemite arv used for calculating library size
  RNA_counts = read.table("/gpfs/hpchome/a72094/hpc/projects/RNAseq_pipeline/results/expression_matrices/featureCounts/GEUVADIS.tsv.gz", header = TRUE)
  RNA_meta_df = wiggleplotrConstructMetadata(RNA_counts, RNA_sample_metadata, "/gpfs/hpc/home/a72094/projects/RNAseq_pipeline/processed/GEUVADIS/bigwig", bigWig_suffix = ".str1.bw", condition_name_levels = c("naive"))
  rm(RNA_counts)
  saveRDS(RNA_meta_df, paste0(path, 'RNA_meta_df'))
}
```

```{r}
RNA_meta_df = readRDS(paste0(path, 'RNA_meta_df'))
RNA_meta_df
```


```{r}
regions_df = unique(filtered[,c('gene_id', 'peak_chr','gene_start', 'gene_end','gene_snpid','gene_snp_pos')])
colnames(regions_df) = c('gene_id', 'chr', 'start', 'end', 'rs_id', 'pos')
regions_df
```

geenide annotatsioonid ja transkriptide annotatsioonid
```{r}
if (FALSE){
  gtf_df <- as.data.frame(rtracklayer::import(paste0(path, 'genotypes/Homo_sapiens.GRCh38.96.chr.gtf.gz')))
  filtered_annotations = gtf_df[gtf_df$gene_id %in% regions_df$gene_id,]
  filtered_annotations
  rm(gtf_df)
  saveRDS(filtered_annotations, paste0(path, 'filtered_annotations'))
}
```


```{r}
filtered_annotations = readRDS(paste0(path, 'filtered_annotations'))
filtered_annotations
```


Koik geeni eksonid GRanges objektidena
```{r}
find_peak_annotations = function(region_coords, chr, RNA_peak_metadata){
  RNA_peak_annot = wiggleplotrExtractPeaks(region_coords, chrom = chr, RNA_peak_metadata)
  RNA_peak_annot$peak_annot$transcript_id='RNA'
  RNA_peak_annot$peak_annot$gene_id = 'RNA'
  RNA_peak_annot$peak_annot$gene_name = 'RNA-seq'
  names(RNA_peak_annot$peak_list) = 'RNA'
  return(RNA_peak_annot)
}
```

Leian vcf failist loigu, mis vastab uuritavale geenile
```{r}
find_genotype = function(gene_id, region_coords){
  library(GenomicRanges)
  gene_range = GRanges(seqnames =filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'seqnames'], strand = c("*"), ranges = IRanges(start = region_coords[1], end = region_coords[2], names = gene_id))
  vcf_file = paste0(path, 'genotypes/GEUVADIS_GRCh38_filtered.vcf.gz')
  genotype = scanTabixDataFrame(vcf_file, gene_range, col_names = FALSE)[[1]]
  #saveRDS(genotype, paste0(path, 'genotype'))
  
  #zgrep -m 1 "#CHROM" GEUVADIS_GRCh38_filtered.vcf.gz > vcf_genotype_id
  #vim vcf_genotype_id delete #
  
  v = scan(paste0(path, '/genotypes/vcf_genotype_id'), character(), quote = "")
  #unlink(paste0(path, '/genotypes/vcf_genotype_id'))
  colnames(genotype) = v
  
  genotype[genotype=="0|0"]<-0
  genotype[genotype=="1|0"]<-1
  genotype[genotype=="0|1"]<-1
  genotype[genotype=="1|1"]<-2
  
  return(genotype)
}
```


```{r}
find_track_data = function(genotype, chr, RNA_meta_df){
  # add colour group
  colour_group = genotype[which(genotype$CHROM==chr & genotype$POS==pos), ] %>% select(10:454) %>% gather(., key= colnames(.), value=.[1,])
  colnames(colour_group) = c('genotype_id', 'colour_group')
  
  RNA_track_data = dplyr::left_join(RNA_meta_df, colour_group, by='genotype_id') %>% dplyr::mutate(track_id = "RNA-seq")
  RNA_track_data[, 'colour_group'] = as.factor(RNA_track_data[, 'colour_group'])
  return(RNA_track_data)
}
```


ATAC joonised
```{r}
joint_plotlist = list()

for (i in 1:dim(regions_df)[1]){
  ATAC_rs_id = regions_df[i, 'peak_rs_id']
  ATAC_region_coords=c(regions_df[i,'peak_start']-1000, regions_df[i,'peak_end']+1000)
  chr = regions_df[i,'peak_chr']
  ATAC_peak_annot = wiggleplotrExtractPeaks(region_coords, chrom = chr, ATAC_peak_metadata)
  
  # add colour group
  ATAC_colour_group = data.frame(genotype_id = names(vcf_file$genotypes[ATAC_rs_id,]), colour_group=vcf_file$genotypes[rs_id,])
  ATAC_track_data = dplyr::left_join(ATAC_meta_df, ATAC_colour_group, by='genotype_id') %>% dplyr::mutate(track_id = "ATAC-seq")
  ATAC_track_data[, 'colour_group'] = as.factor(ATAC_track_data[, 'colour_group'])
  
  ATAC_coverage = plotCoverage(exons = ATAC_peak_annot$peak_list, cdss = ATAC_peak_annot$peak_list, track_data = ATAC_track_data, rescale_introns = FALSE, 
                             transcript_annotations = ATAC_peak_annot$peak_annot, fill_palette = getGenotypePalette(),
                             connect_exons = FALSE, transcript_label = FALSE, plot_fraction = 0.1, heights = c(0.7,0.3), 
                             region_coords = ATAC_region_coords, return_subplots_list = TRUE, coverage_type = "line")
  
  
  joint_plot = cowplot::plot_grid(ATAC_coverage$coverage_plot, 
                                ATAC_coverage$tx_structure,
                                align = "v", ncol = 1, rel_heights = c(3,2))
  
  joint_plotlist[[i]] = joint_plot
  print(joint_plot)
  ggsave(paste0('/gpfs/hpchome/evelin95/plots/ca_splicing/',regions_df[i,'peak_id'],'.pdf'))
  
}
```

RNA joonised
```{r}
library(ggplot2)
RNA_joint_plotlist = list()
for (i in 1:dim(regions_df)[1]){
  gene_rs_id = regions_df[i, 'gene_rs_id']
  pos = regions_df[i, 'gene_pos']
  gene_id = regions_df[i, 'gene_id']
  gene_region_coords = c(filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'start'], filtered_annotations[which(filtered_annotations$type=='gene' & filtered_annotations$gene_id ==gene_id),'gene_end'])
  chr = regions_df[i,'gene_chr']
  
  RNA_peak_metadata = filtered_annotations[which(filtered_annotations$type=='exon' & filtered_annotations$gene_id==gene_id),c("seqnames", "start", "end", "strand")]
  colnames(RNA_peak_metadata)[1]='chr'
  
  genotype=find_genotype(gene_id, gene_region_coords)
  
  RNA_peak_annot = find_peak_annotations(gene_region_coords, chr, RNA_peak_metadata)
  RNA_track_data = find_track_data(genotype, chr, RNA_meta_df)
  
  if (sum(is.na(RNA_track_data$colour_group))<100){
  RNA_coverage = plotCoverage(exons = RNA_peak_annot$peak_list, cdss = RNA_peak_annot$peak_list, track_data = RNA_track_data, rescale_introns = TRUE, 
                             transcript_annotations = RNA_peak_annot$peak_annot, fill_palette = getGenotypePalette(),
                             connect_exons = FALSE, transcript_label = FALSE, plot_fraction = 0.1, heights = c(0.7,0.3), 
                             region_coords = region_coords, return_subplots_list = TRUE, coverage_type = "line")
  
  
  joint_plot = cowplot::plot_grid(RNA_coverage$coverage_plot, 
                                RNA_coverage$tx_structure,
                                align = "v", ncol = 1, rel_heights = c(3,2))

  RNA_joint_plotlist[[i]] = joint_plot
  print(joint_plot)
  ggsave(paste0('/gpfs/hpchome/evelin95/plots/ca_splicing/',gene_id,'.pdf'))  
  }
}
```



